Predictive visual servoing; uncertainty analysis and probabilistic robust frameworks
type: CreativeWork
description: Motion control of robots in unstructured environments is a challenging task. The utilization of cameras as an information-rich sensor shows promise. In this context, image-based visual predictive controllers have gained attention due to their optimal-ity and constraint-handling capabilities. However, their performance deteriorates in presence of uncertainties in the robotic platforms, system models, and measurements. This work proposes a set of robust image-based visual predictive control methods that overcome the shortcomings of the previous visual servoing methods in the presence of uncertainties. In this dissertation, we have proposed a set of adaptive, stochastic, risk-averse, and learning-based visual servoing schemes that improve the performance and constraint compliance of visual servoing systems compared to their classical coun-terparts. The validity of the proposed control frameworks has been evaluated on a 6-DOF serial industrial manipulator and a model unmanned aerial vehicles via various experiments and simulations.
abstract: Motion control of robots in unstructured environments is a challenging task. The utilization of cameras as an information-rich sensor shows promise. In this context, image-based visual predictive controllers have gained attention due to their optimal-ity and constraint-handling capabilities. However, their performance deteriorates in presence of uncertainties in the robotic platforms, system models, and measurements. This work proposes a set of robust image-based visual predictive control methods that overcome the shortcomings of the previous visual servoing methods in the presence of uncertainties. In this dissertation, we have proposed a set of adaptive, stochastic, risk-averse, and learning-based visual servoing schemes that improve the performance and constraint compliance of visual servoing systems compared to their classical coun-terparts. The validity of the proposed control frameworks has been evaluated on a 6-DOF serial industrial manipulator and a model unmanned aerial vehicles via various experiments and simulations.
author: Sajjadi, Sina
language: en
date published: 2023-06
date modified: 2026-07-24T04:03:41Z
provider: University of Regina
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Faculty of Graduate Studies and Research, University of Regina

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10294/16179

PropertyValue
property ID: DOI
value: 10.82465/4565